{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "使用auto_ins作如下分析\n",
    "1、首先对loss重新编码为1/0，有数值为1，命名为loss_flag\n",
    "2、对loss_flag分布情况进行描述分析\n",
    "3、分析是否出险和年龄、驾龄、性别、婚姻状态等变量之间的关系(提示:使用分类盒须图,堆叠柱形图)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "data": {
      "text/plain": "   EngSize  Age Gender Marital  exp Owner  vAge Garage AntiTFD import  Loss\n0      2.0   56      男      已婚   20    公司    10      有   有防盗装置     进口   0.0\n1      1.8   41      男      已婚   20    公司     9      有   无防盗装置     国产   0.0\n2      2.0   44      男      未婚   20    公司     8      有   有防盗装置     国产   0.0\n3      1.6   56      男      已婚   20    公司     7      有   有防盗装置     国产   0.0\n4      1.8   45      男      已婚   20    公司     7      无   无防盗装置     国产   0.0",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>EngSize</th>\n      <th>Age</th>\n      <th>Gender</th>\n      <th>Marital</th>\n      <th>exp</th>\n      <th>Owner</th>\n      <th>vAge</th>\n      <th>Garage</th>\n      <th>AntiTFD</th>\n      <th>import</th>\n      <th>Loss</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2.0</td>\n      <td>56</td>\n      <td>男</td>\n      <td>已婚</td>\n      <td>20</td>\n      <td>公司</td>\n      <td>10</td>\n      <td>有</td>\n      <td>有防盗装置</td>\n      <td>进口</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.8</td>\n      <td>41</td>\n      <td>男</td>\n      <td>已婚</td>\n      <td>20</td>\n      <td>公司</td>\n      <td>9</td>\n      <td>有</td>\n      <td>无防盗装置</td>\n      <td>国产</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2.0</td>\n      <td>44</td>\n      <td>男</td>\n      <td>未婚</td>\n      <td>20</td>\n      <td>公司</td>\n      <td>8</td>\n      <td>有</td>\n      <td>有防盗装置</td>\n      <td>国产</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.6</td>\n      <td>56</td>\n      <td>男</td>\n      <td>已婚</td>\n      <td>20</td>\n      <td>公司</td>\n      <td>7</td>\n      <td>有</td>\n      <td>有防盗装置</td>\n      <td>国产</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.8</td>\n      <td>45</td>\n      <td>男</td>\n      <td>已婚</td>\n      <td>20</td>\n      <td>公司</td>\n      <td>7</td>\n      <td>无</td>\n      <td>无防盗装置</td>\n      <td>国产</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "data = pd.read_csv(r'../data/auto_ins.csv', encoding='gbk')\n",
    "data.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-18T21:48:18.366268Z",
     "end_time": "2024-06-18T21:48:18.415738Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "      EngSize  Age Gender Marital  exp Owner  vAge Garage AntiTFD import  \\\n0         2.0   56      男      已婚   20    公司    10      有   有防盗装置     进口   \n1         1.8   41      男      已婚   20    公司     9      有   无防盗装置     国产   \n2         2.0   44      男      未婚   20    公司     8      有   有防盗装置     国产   \n3         1.6   56      男      已婚   20    公司     7      有   有防盗装置     国产   \n4         1.8   45      男      已婚   20    公司     7      无   无防盗装置     国产   \n...       ...  ...    ...     ...  ...   ...   ...    ...     ...    ...   \n4228      1.8   22      女      未婚    0    私人     1      有   有防盗装置     国产   \n4229      2.5   22      男      未婚    0    私人     1      有   无防盗装置     进口   \n4230      1.8   21      男      未婚    0    私人     1      有   无防盗装置     国产   \n4231      1.8   21      女      未婚    0    私人     1      有   无防盗装置     进口   \n4232      2.4   21      女      未婚    0    私人     1      有   无防盗装置     进口   \n\n        Loss  loss_flag  \n0        0.0          0  \n1        0.0          0  \n2        0.0          0  \n3        0.0          0  \n4        0.0          0  \n...      ...        ...  \n4228   976.0          1  \n4229   855.6          1  \n4230     0.0          0  \n4231  3328.0          1  \n4232  1564.0          1  \n\n[4233 rows x 12 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>EngSize</th>\n      <th>Age</th>\n      <th>Gender</th>\n      <th>Marital</th>\n      <th>exp</th>\n      <th>Owner</th>\n      <th>vAge</th>\n      <th>Garage</th>\n      <th>AntiTFD</th>\n      <th>import</th>\n      <th>Loss</th>\n      <th>loss_flag</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2.0</td>\n      <td>56</td>\n      <td>男</td>\n      <td>已婚</td>\n      <td>20</td>\n      <td>公司</td>\n      <td>10</td>\n      <td>有</td>\n      <td>有防盗装置</td>\n      <td>进口</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.8</td>\n      <td>41</td>\n      <td>男</td>\n      <td>已婚</td>\n      <td>20</td>\n      <td>公司</td>\n      <td>9</td>\n      <td>有</td>\n      <td>无防盗装置</td>\n      <td>国产</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2.0</td>\n      <td>44</td>\n      <td>男</td>\n      <td>未婚</td>\n      <td>20</td>\n      <td>公司</td>\n      <td>8</td>\n      <td>有</td>\n      <td>有防盗装置</td>\n      <td>国产</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.6</td>\n      <td>56</td>\n      <td>男</td>\n      <td>已婚</td>\n      <td>20</td>\n      <td>公司</td>\n      <td>7</td>\n      <td>有</td>\n      <td>有防盗装置</td>\n      <td>国产</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.8</td>\n      <td>45</td>\n      <td>男</td>\n      <td>已婚</td>\n      <td>20</td>\n      <td>公司</td>\n      <td>7</td>\n      <td>无</td>\n      <td>无防盗装置</td>\n      <td>国产</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4228</th>\n      <td>1.8</td>\n      <td>22</td>\n      <td>女</td>\n      <td>未婚</td>\n      <td>0</td>\n      <td>私人</td>\n      <td>1</td>\n      <td>有</td>\n      <td>有防盗装置</td>\n      <td>国产</td>\n      <td>976.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4229</th>\n      <td>2.5</td>\n      <td>22</td>\n      <td>男</td>\n      <td>未婚</td>\n      <td>0</td>\n      <td>私人</td>\n      <td>1</td>\n      <td>有</td>\n      <td>无防盗装置</td>\n      <td>进口</td>\n      <td>855.6</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4230</th>\n      <td>1.8</td>\n      <td>21</td>\n      <td>男</td>\n      <td>未婚</td>\n      <td>0</td>\n      <td>私人</td>\n      <td>1</td>\n      <td>有</td>\n      <td>无防盗装置</td>\n      <td>国产</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4231</th>\n      <td>1.8</td>\n      <td>21</td>\n      <td>女</td>\n      <td>未婚</td>\n      <td>0</td>\n      <td>私人</td>\n      <td>1</td>\n      <td>有</td>\n      <td>无防盗装置</td>\n      <td>进口</td>\n      <td>3328.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4232</th>\n      <td>2.4</td>\n      <td>21</td>\n      <td>女</td>\n      <td>未婚</td>\n      <td>0</td>\n      <td>私人</td>\n      <td>1</td>\n      <td>有</td>\n      <td>无防盗装置</td>\n      <td>进口</td>\n      <td>1564.0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>4233 rows × 12 columns</p>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['loss_flag'] = data['Loss'].map(lambda x: 1 if x > 0 else 0)\n",
    "data"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-18T21:48:18.419558Z",
     "end_time": "2024-06-18T21:48:18.439421Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "loss_flag\n0    3028\n1    1205\nName: count, dtype: int64"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['loss_flag'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-18T21:48:18.439421Z",
     "end_time": "2024-06-18T21:48:18.524798Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "loss_flag\n0    0.715332\n1    0.284668\nName: count, dtype: float64"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['loss_flag'].value_counts() / data['loss_flag'].count()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-18T21:48:18.455697Z",
     "end_time": "2024-06-18T21:48:18.563769Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='loss_flag'>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['loss_flag'].value_counts().plot(kind='bar')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-18T21:48:18.471472Z",
     "end_time": "2024-06-18T21:48:18.649791Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Axes: xlabel='loss_flag', ylabel='exp'>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(1, 2, 1)\n",
    "ax2 = fig.add_subplot(1, 2, 2)\n",
    "sns.boxplot(x='loss_flag', y='Age', data=data, ax=ax1)\n",
    "sns.boxplot(x='loss_flag', y='exp', data=data, ax=ax2)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-18T21:48:18.649791Z",
     "end_time": "2024-06-18T21:48:19.020626Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from stack2dim import *\n",
    "\n",
    "stack2dim(data, 'Gender', 'loss_flag')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-18T21:48:19.022806Z",
     "end_time": "2024-06-18T21:48:19.184798Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stack2dim(data, 'Marital', 'loss_flag')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-18T21:48:19.184798Z",
     "end_time": "2024-06-18T21:48:19.301665Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-18T21:48:19.301665Z",
     "end_time": "2024-06-18T21:48:19.317810Z"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
